• Survey Research
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• v.7 no.1
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• pp.55-84
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• 2006

This study has classified development stages (Embryonic-Growth-Maturity) of mobile telecommunication industry based on Industry Life Cycle theory. There are two steps to be analyzed in this study, In the first step, cluster was investigated through cluster analysis using mobile density to categorize development stages of mobile telecommunication industry. In the second step, we compared on indexes of market structure, market efficiency and market performance to find out characteristics of each stage of development. The results are as follows. First, HHI is higher at embryonic stage than at growth and maturity stages, Second, ARPU(Average Revenue Per User) and RPM(Revenue Per Minute) are getting higher as the stages move on. Third, EBITDA margins, an index of market performance, is decreasing along the three stages. Finally, this study presents a clue to define the stage of development of mobile telecommunication industry and build a proper strategy for the market change.

• Gut and Liver
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• v.12 no.6
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• pp.714-721
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• 2018

Background/Aims: Recently reported prognostic models for primary biliary cholangitis (PBC) have been shown to be effective in Western populations but have not been well-validated in Asian patients. This study aimed to compare the performance of prognostic models in Korean patients and to investigate whether inflammation-based scores can further help in prognosis prediction. Methods: This study included 271 consecutive patients diagnosed with PBC in Korea. The following prognostic models were evaluated: the Barcelona model, the Paris-I/II model, the Rotterdam criteria, the GLOBE score and the UK-PBC score. The neutrophil-to-lymphocyte ratio (NLR) was analyzed with reference to its association with prognosis. Results: For predicting liver transplant or death at the 5-year and 10-year follow-up examinations, the UK-PBC score (areas under the receiver operating characteristic curve [AUCs], 0.88 and 0.82) and GLOBE score (AUCs, 0.85 and 0.83) were significantly more accurate in predicting prognosis than the other scoring systems (all p<0.05). There was no significant difference between the performance of the UK-PBC and GLOBE scores. In addition to the prognostic models, a high NLR (>2.46) at baseline was an independent predictor of reduced transplant-free survival in the multivariate analysis (adjusted hazard ratio, 3.74; p<0.01). When the NLR was applied to the prognostic models, it significantly differentiated the prognosis of patients. Conclusions: The UK-PBC and GLOBE scores showed good prognostic performance in Korean patients with PBC. In addition, a high NLR was associated with a poorer prognosis. Including the NLR in prognostic models may further help to stratify patients with PBC.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.115-137
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• 2023

This study analyzed the relationship between organizational innovative capability and absorptive capacity, market and technology orientations, and their impact on business performance for IT-BPO companies that are required to absorb new technologies from a leading perspective in the digital transformation era. To achieve this, an online specialized research company and offline surveys were conducted on 291 domestic IT-BPO companies, and SPSS 23 was used for descriptive statistics and reliability analysis while AMOS 23 was used for hypothesis testing including validity and mediating effects. The main findings were as follows: First, in the relationship between innovation and absorptive capabilities and Market Orientation Strategic(MOS), learning capability and knowledge network capability were found to have a statistically significant positive (+) effect on MOS. In the relationship between innovation and absorptive capabilities and Technology Orientation Strategic(TOS), R&D capability, potential absorptive capacity, and realized absorptive capacity had a statistically significant positive (+) effect on TOS. Second, in the relationship between innovation and absorptive capabilities and BP, only R&D capability was found to have a significant effect on BP. Third, both market orientation and technology orientation were found to have a significant positive (+) effect on BP. These findings suggest that effective competency factors can be identified according to the market and technology orientations pursued by IT-BPO companies to increase their growth and value creation, and provide implications for developing differentiated competency enhancement strategies based on strategic objectives.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.273-280
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• 2005

Robust magneto-telluric (MT) response function estimators are now in standard use in electromagnetic induction research. Properly devised and applied, these methods can reduce the influence of unusual data (outlier) in the response (electric field) variable, but often not sensitive to exceptional predictor (magnetic field) data, which are termed leverage points. A bounded influence estimator is described which simultaneously limits the influence of both outlier and leverage point, and has proven to consistently yield more reliable MT response function estimates than conventional robust approach. The bounded influence estimator combines a standard robust M-estimator with leverage weighting based on the statistics of the hat matrix diagonal, which is a standard statistical measure of unusual predictors. Further extensions to MT data analysis are proposed, including a establishment of data rejection criterion which minimize the influence of both electric and magnetic outlier in frequency domain based on statistical distribution of electromagnetic field. The rejection scheme made in this study seems to have an effective performance on eliminating extreme data, which is even not removed by BI estimator, in frequency domain. The effectiveness and advantage of these developments are illustrated using real MT data.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1031-1041
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• 2018

ANFIS-based methodology for improving GloSea5 ensemble weather forecast is developed and evaluated in this study. The proposed method consists of two steps: pre & post processing. For ensemble prediction of GloSea5, weights are assigned to the ensemble members based on Optimal Weighting Method (OWM) in the pre-processing. Then, the bias of the results of pre-processed is corrected based on Model Output Statistics (MOS) method in the post-processing. The watershed of the Chungju multi-purpose dam in South Korea is selected as a study area. The results of evaluation indicated that the pre-processing step (CASE1), the post-processing step (CASE2), pre & post processing step (CASE3) results were significantly improved than the original GloSea5 bias correction (BC_GS5). Correction performance is better the order of CASE3, CASE1, CASE2. Also, the accuracy of pre-processing was improved during the season with high variability of precipitation. The post-processing step reduced the error that could not be smoothed by pre-processing step. It could be concluded that this methodology improved the ability of GloSea5 ensemble weather forecast by using ANFIS, especially, for the summer season with high variability of precipitation when applied both pre- and post-processing steps.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.105-122
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• 2022

According to the statistics about the fatal crashes that have occurred on the expressways for the last 5 years, those who died on the shoulders of the road has been as 3 times high as the others who died on the expressways. It suggests that the crashes on the shoulders of the road should be fatal, and that it would be important to prevent the traffic crashes by cracking down on the vehicles intruding the shoulders of the road. Therefore, this study proposed a method to detect a vehicle that violates the shoulder lane by using the Faster R-CNN. The vehicle was detected based on the Faster R-CNN, and an additional reading module was configured to determine whether there was a shoulder violation. For experiments and evaluations, GTAV, a simulation game that can reproduce situations similar to the real world, was used. 1,800 images of training data and 800 evaluation data were processed and generated, and the performance according to the change of the threshold value was measured in ZFNet and VGG16. As a result, the detection rate of ZFNet was 99.2% based on Threshold 0.8 and VGG16 93.9% based on Threshold 0.7, and the average detection speed for each model was 0.0468 seconds for ZFNet and 0.16 seconds for VGG16, so the detection rate of ZFNet was about 7% higher. The speed was also confirmed to be about 3.4 times faster. These results show that even in a relatively uncomplicated network, it is possible to detect a vehicle that violates the shoulder lane at a high speed without pre-processing the input image. It suggests that this algorithm can be used to detect violations of designated lanes if sufficient training datasets based on actual video data are obtained.

• Transactions on Electrical and Electronic Materials
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• v.15 no.5
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• pp.241-244
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• 2014

Charge trap flash memory capacitors incorporating $(HfO_2)_x(Al_2O_3)_{1-x}$ film, as the charge trapping layer, were fabricated. The effects of the charge trapping layer composition on the memory characteristics were investigated. It is found that the memory window and charge retention performance can be improved by adding Al atoms into pure $HfO_2$; further, the memory capacitor with a $(HfO_2)_{0.9}(Al_2O_3)_{0.1}$ charge trapping layer exhibits optimized memory characteristics even at high temperatures. The results should be attributed to the large band offsets and minimum trap energy levels. Therefore, the $(HfO_2)_{0.9}(Al_2O_3)_{0.1}$ charge trapping layer may be useful in future nonvolatile flash memory device application.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.355-361
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• 2012

Gwangdong Dam Watershed is affected by the increased discharge caused by the melting snow in the spring season. Therefore, simulation results obtained using hydrologic models have generally been inaccurate in relation to discharge without snow pack and melt modules. In this research, a grid based distributed rainfall runoff model (K-DRUM) was developed using a snow pack and melt module, and has been applied in the Gwangdong Dam Watershed to simulate the discharge for a four year period. A previous version of K-DRUM, which does not include a snow pack or melt module, was used to calculate the discharge in order to compare the snow melt effect. The simulation period lasted about 7 months from October of the previous year to April of this year using hourly precipitation and weather observed data. To evaluate the model performance, NSE, PBIAS and RSR statistics techniques were applied using the simulation results of the discharge. From the results of reliability evaluation, the K-DRUM model, which uses a snow pack and melt module, had a good applicability for the runoff simulation considering the snow melt effect in the spring.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.485-491
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• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.38-45
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• 2020

This study presents a systematic procedure for developing a short-term prediction deep learning model of rebar price using bidirectional LSTM, Random Search, data combination, Dropout. In general, users intuitively determine these values, making it time-consuming and repetitive attempts to explore results with good predictive performance, and the results found by these attempts cannot be guaranteed to be excellent. With the proposed approach presented in this study, the average accuracy of short-term price forecasts is approximately 98.32%. In addition, this approach could be used as basic data to produce good predictive results in a study that predicts prices with time series data based on statistics, including building materials other than rebars.